Knowledge Cores in Large Formal Contexts
| Autor: | Tom Hanika, Johannes Hirth |
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| Rok vydání: | 2020 |
| Předmět: |
Wissensbasis
FOS: Computer and information sciences Computer Science - Logic in Computer Science 68T30 03G10 97R40 Computer Science - Artificial Intelligence Applied Mathematics Formale Begriffsanalyse implications Logic in Computer Science (cs.LO) formal concept analysis Artificial Intelligence (cs.AI) Artificial Intelligence lattices k-cores Verband knowledge base Bi-partite graphs Wissensmanagement |
| DOI: | 10.48550/arxiv.2002.11776 |
| Popis: | Knowledge computation tasks are often infeasible for large data sets. This is in particular true when deriving knowledge bases in formal concept analysis (FCA). Hence, it is essential to come up with techniques to cope with this problem. Many successful methods are based on random processes to reduce the size of the investigated data set. This, however, makes them hardly interpretable with respect to the discovered knowledge. Other approaches restrict themselves to highly supported subsets and omit rare and interesting patterns. An essentially different approach is used in network science, called $k$-cores. These are able to reflect rare patterns if they are well connected in the data set. In this work, we study $k$-cores in the realm of FCA by exploiting the natural correspondence to bi-partite graphs. This structurally motivated approach leads to a comprehensible extraction of knowledge cores from large formal contexts data sets. Comment: 13 pages, 10 figures |
| Databáze: | OpenAIRE |
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